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Home Uncertainty & Complexity

The Fault In Our Letters

Daily Remedy by Daily Remedy
August 8, 2021
in Uncertainty & Complexity
0

How we think, we talk, just as how we talk, we think.

Cause is effect, just as effect is cause.

It was never meant to be linear or rational, because how we think is not linear nor rational.

So when the WHO recently announced that it will change the nomenclature around COVID-19 variants, many responded with joy, and some with exacerbated relief – acknowledging the change was long overdue.

“No country should be stigmatized for detecting and reporting variants”, the WHO’s COVID-19 technical lead, Dr. Maria Van Kerkhove, said in a recent announcement while presenting the new nomenclature. Yet this was precisely what had happened with the original, cumbersome system – a system rife with complex sequences of number and letters pieced together like a modern day Morse code.

Invariably people simplified the terminology, simplified it according to the biases they held. Soon B.1.351 became “South African”, and P.1 became “Brazilian”. Undoubtedly a discriminatory manner of thinking in which the geographic locale from where the virus variant first emerged is blamed for the variant itself – an error of attribution.

A manner of thinking that has not only characterized our view of the pandemic, but of healthcare at large. Something revealed during the pandemic that is arguably more important that the virus itself – healthcare is filled with systemic discrimination, often reflected in how we understand healthcare.

Healthcare is complex, and in our attempt to understand it, we simplify it. But in simplifying, we give rise to the biases in our thinking, the subtle default mechanisms of thought that appear subconsciously, but then loom large in our conscious predilections.

When we hear the word, ‘cancer’, we immediately think thoughts of life or death, of mortality and of critical end of life decisions. Yet cancer is but one of many words that describe a complex autoimmune process. Not every cancer is life threatening. In fact most are not.

But our association with the word impacts our perceptions when we hear it. We simplify all of cancer into its most evocative connotations, and consequently, that is only – or at least initially – how we view anything related to cancer.

So what does it mean when we associate a variant of a virus with a particular location, and then cast aspersions upon the people within that location?

It means we simplify to the lowest common denominator when it comes to healthcare. That disease and disease burden are susceptible to racial prejudices and systemic discriminatory practices.

We are all guilty of it.

We see it when a female patient attributes her excessive emotions to her menstrual cycle.

We see it when an elderly male patient attributes his testosterone levels to his fatigue or sexual ineptitude.

We see it when charity funds addressing diseases associated with affluent white populations receive more donations compared to charity funds addressing diseases associated with ethnic minorities. Look at the donations the Cystic Fibrosis Foundation pulls in compared to the Sickle Cell Society – the discrepancy is no statistical anomaly.

It reflects the associations we construct in our minds, the patterns of thought we develop through our healthcare experiences – those experienced directly and those observed second and third hand. Associations that hearken to the parts of our mind where discrimination forms, and where it is expressed.

Associations far more complex and subtle than overt discrimination, far less discrete than racism, sexism, or other obvious forms of discrimination – that explain why certain populations, certain demographics have worse outcomes relative to the average.

But when we press for any one particular reason, or any one attributable cause, we struggle to find a direct answer. And in our ignorance, we allow our most ignorant tendencies to manifest.

This is how complex patterns of letters and numbers describing a variant of a virus came to describe prejudices against a country or a region.

Most of us struggle to understand how viruses mutate or spread, and in our ignorance we attribute the place of origin as the culprit – simplifying proximity to the origin of the variant as culpability in giving rise to the variant.

A gross oversimplification, bore out of ignorance of facts.

Something that took a year of enduring a global pandemic to figure out.

Something we have yet to figure out for healthcare at large.

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Daily Remedy

Daily Remedy

Dr. Jay K Joshi serves as the editor-in-chief of Daily Remedy. He is a serial entrepreneur and sought after thought-leader for matters related to healthcare innovation and medical jurisprudence. He has published articles on a variety of healthcare topics in both peer-reviewed journals and trade publications. His legal writings include amicus curiae briefs prepared for prominent federal healthcare cases.

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In this episode, the host discusses the significance of large language models (LLMs) in healthcare, their applications, and the challenges they face. The conversation highlights the importance of simplicity in model design and the necessity of integrating patient feedback to enhance the effectiveness of LLMs in clinical settings.

Takeaways
LLMs are becoming integral in healthcare.
They can help determine costs and service options.
Hallucination in LLMs can lead to misinformation.
LLMs can produce inconsistent answers based on input.
Simplicity in LLMs is often more effective than complexity.
Patient behavior should guide LLM development.
Integrating patient feedback is crucial for accuracy.
Pre-training models with patient input enhances relevance.
Healthcare providers must understand LLM limitations.
The best LLMs will focus on patient-centered care.

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00:00 Introduction to LLMs in Healthcare
05:16 The Importance of Simplicity in LLMs
The Future of LLMs in HealthcareDaily Remedy
YouTube Video U1u-IYdpeEk
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Health systems are increasingly deploying ambient artificial intelligence tools that listen to clinical encounters and automatically generate draft visit notes. These systems are intended to reduce documentation burden and allow clinicians to focus more directly on patient interaction. At the same time, they raise unresolved questions about patient consent, data handling, factual accuracy, and legal responsibility for machine‑generated records. Recent policy discussions and legal actions suggest that adoption is moving faster than formal oversight frameworks. The practical clinical question is...

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